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Title:

Methods for neural network based prediction of roughness-induced crossflow-like vorticity in re-entry scenarios

Document type:
Zeitschriftenaufsatz
Author(s):
Ulrich, Friedrich; Sedlmeyr, Thomas; Stemmer, Christian
Abstract:
Laminar-turbulent transition plays a critical role in the design of re-entry vehicles. This study investigates roughness-induced transition caused by a randomly distributed roughness patch. In the wake of the roughness patch, a cross-flow-like vortex is formed by the distributed roughness. The cross-flow-like vortex generates regions of strong wall-normal and spanwise gradients in the streamwise velocity where unstable acoustic modes are strongly amplified leading to transition. This study is us...     »
Keywords:
Machine learning Laminar-turbulent transition High-enthalpy boundary-layer flows Saliency maps Distributed roughness
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
International Journal of Heat and Fluid Flow
Year:
2024
Journal volume:
107
Pages contribution:
109375
Covered by:
Scopus
Language:
en
Fulltext / DOI:
doi:10.1016/j.ijheatfluidflow.2024.109375
WWW:
https://www.sciencedirect.com/science/article/pii/S0142727X24001000
Publisher:
Elsevier BV
E-ISSN:
0142-727X
Notes:
Acknowledgments This research was supported by funds from the TUM International Graduate School of Science and Engineering (IGSSE) and the Cusanuswerk e.V scholarship. Further, the authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu ) for supporting this project by providing computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de).
Submitted:
15.06.2023
Accepted:
04.04.2024
Date of publication:
01.07.2024
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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